The rapidly increasing sensitivity of gravitational wave detectors is enabling the detection of a growing number of compact binary mergers. These events are crucial for understanding the population properties of compact binaries. However, many previous studies rely on computationally expensive inference frameworks, limiting their scalability. In this work, we present gwkokab, a jax-based framework that enables modular model building with independent rate for each subpopulation such as binary black hole, binary neutron star, and neutron star black hole binary. It provides accelerated inference using the normalizing flow based sampler called flowmc and is also compatible with numpyro samplers. To validate our framework, we generated two synthetic populations, one comprising spinning eccentric binaries and the other circular binaries using a multisource model. We then recovered their injected parameters at significantly reduced computational cost and demonstrated that eccentricity distribution can be recovered even in spinning eccentric populations. We also reproduced results from two prior studies: one on nonspinning eccentric populations, and another on the binary black hole mass distribution using the fourth Gravitational-Wave Transient Catalog. We anticipate that gwkokab will not only reduce computational costs but also enable more detailed multipopulation analyses of mass, spin, eccentricity, and redshift distributions, offering deeper insights into compact binary formation and evolution.

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• Gravitational waves